Micro- and Nano-Scales Three-Dimensional Characterisation of Softwood
Abstract
:1. Introduction
2. Materials and Methods
2.1. Samples Preparation
2.2. Imaging Setups
2.3. Dynamic Vapour Sorption Analysis
2.4. Methods of Analysis
- (A).
- The wood porosity, due to the presence of the lumen, was defined here as void volume per total volume. By grey value thresholding, the tomographic datasets were first converted into binary datasets composed of voxels containing either cell wall material or air. The “region growing” algorithm available in VGStudio MAX 2.0 software was used as a thresholding method. Starting from the binary images, the wood volume was calculated by counting the number of material (white) voxels for each slice, summing up over all the stacked slices, and multiplying this value by the voxel size.
- (B).
- The wood cell wall deformations were calculated both at the global level (such as during free swelling/shrinkage) and at the local level (as observed during the restraining experiment). In particular, for case 1.b, two ROIs were selected for each sample containing different restraint intensity to the free swelling from the restraining device. In detail, regions of the sample not directly in contact with the PMMA cube surface received a minor restraint to the swelling during moisture adsorption caused by the device; such regions are called “ROIs 1”. Conversely, regions directly in touch with the PMMA cube surface were more subjected to the restraining role of the device: such regions are referred to as “ROIs 2”. The most commonly used transformation method for the calculation of the global deformations is the so-called affine transformation, where changes in position, size and shape of a volume are described. The parameterisation of a 3D affine transformation generally involves 12 parameters (three for defining translation, three for rotation, three for scaling and three for shear). Details are published in [8,10]. A previous work of the author [21] describes the quantitative method implemented to analyse the local deformations in cellular wood tissues when subjected to environmental changes. The algorithm, written in Matlab, employs a free-form deformation model based on B-splines. This algorithm was then used to detect the local strains in bordered pits as well.
3. Results
3.1. Wood Porosity
3.2. Anisotropic Swelling Behaviour of Earlywood and Latewood
3.3. Anatomy of Bordered Pits in Earlywood Samples
3.4. Behaviour of Bordered Pits during Moisture Sorption
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Study (1) Cases: | Earlywood (EW) | Latewood (LW) | Dimension [T × R × L] |
---|---|---|---|
(1.a) | EW1, EW2 | LW1, LW2 | 500 μm × 500 μm × 8 mm |
(1.b) | EWt | LWt, LWr | 500 μm × 500 μm × 2 mm |
Sample Name | Sample A | Sample B | Sample C |
---|---|---|---|
pits no. | 3 | 1 | 1 |
initial state | dried | green | dried |
dimensions | 50 μm × 50 μm × 3 mm | 50 μm × 50 μm × 3 mm | 50 μm × 50 μm × 3 mm |
Study | Cellular Scale | Sub-Cellular Scale | |
---|---|---|---|
Case 1.a | Case 1.b | Case 2 | |
Detector | PCO 2000 | PCO 2000 | Photonic Science VHR Image Star |
FOV [μm2 × μm2] | 757 × 757 | 757 × 757 | 12 × 12 * |
Binning | 1 | 2 | 1 |
Effective Pixel size [μm] | 0.37 | 0.74 | 0.084 |
Exposure time [ms] | 75 | 40 | 1200 |
SDD [mm] | 30 | 20 | see Figure 3 |
Projections nr. | 1001 | 1001 | 541 |
X-ray Energy [keV] | 20 | 20 | 12 |
Case (Cellular Scale) | Sample Name | Porosity [%] |
---|---|---|
Case 1.a) | EW1 | 78 |
EW2 | 64 | |
LW1 | 50 | |
LW2 | 45 | |
Case 1.b) | EWT | 65 |
LWT | 54 | |
LWR | 60 |
Case 1.a) | Case 1.b) | |||
---|---|---|---|---|
Anisotropy | Free Swelling | Restrained Swelling | ||
1 | 2 | ROI 1 | ROI 2 | |
EW | 3.10 | 1.70 | EWT:2.40 | EWT:0.25 |
LW | 1.30 | 1.10 | LWT:0.80 LWR:5.30 | LWT:0.50 |
State | RH = 10% (ads.) | RH = 50% (ads.) | RH = 50% (des.) | RH = 25% (des.) |
---|---|---|---|---|
k1 ± σ [μm] | 1.00 ± 0.02 | 1.20 ± 0.04 | 1.40 ± 0.03 | 1.42 ± 0.04 |
k2 ± σ [μm] | 1.22 ± 0.05 | 1.36 ± 0.05 | 1.38 ± 0.06 | 1.60 ± 0.08 |
k3 ± σ [μm] | 1.23 ± 0.18 | 1.08 ± 0.04 | 1.05 ± 0.05 | 1.00 ± 0.04 |
k4 ± σ [μm] | 1.54 ± 0.05 | 1.20 ± 0.05 | 1.03 ± 0.05 | 0.90 ± 0.05 |
Measurements | W | D | 2b | 2k |
---|---|---|---|---|
Value ± 0.05 [μm] | 3.40 | 16.40 | 4.25 | 4.50 |
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Patera, A.; Bonnin, A.; Mokso, R. Micro- and Nano-Scales Three-Dimensional Characterisation of Softwood. J. Imaging 2021, 7, 263. https://doi.org/10.3390/jimaging7120263
Patera A, Bonnin A, Mokso R. Micro- and Nano-Scales Three-Dimensional Characterisation of Softwood. Journal of Imaging. 2021; 7(12):263. https://doi.org/10.3390/jimaging7120263
Chicago/Turabian StylePatera, Alessandra, Anne Bonnin, and Rajmund Mokso. 2021. "Micro- and Nano-Scales Three-Dimensional Characterisation of Softwood" Journal of Imaging 7, no. 12: 263. https://doi.org/10.3390/jimaging7120263
APA StylePatera, A., Bonnin, A., & Mokso, R. (2021). Micro- and Nano-Scales Three-Dimensional Characterisation of Softwood. Journal of Imaging, 7(12), 263. https://doi.org/10.3390/jimaging7120263